Towards Effective and Interpretable Deep Learning Applications for Microscopic Medical Imaging

深度学习已经迅速发展到解决许多领域的难题，如计算机视觉，特别是那些有大量数据可用的问题。然而，对于许多医疗应用来说，可用的数据往往是中小型数据集。此外，这些数据集通常是不完整的，包含输入错误，并且在目标分类方面不平衡。对于医学成像数据，除了解剖结构的基本复杂性之外，还存在收集过程中的噪声和成像伪影的挑战。我们研究的长期目标是开发强大、方便、快速、透明和准确的计算机辅助医疗诊断和分级工具，为临床医生提供自动独立的第二意见，帮助避免错误/疏忽。我们提议的研究旨在引入或采用新的深度学习方法来解决与中小型医疗数据的分类、分割和可解释性相关的问题。

Deep learning has quickly evolved to solve difficult problems in many domains such as computer vision, particularly for problems where large amounts of data are available. For many medical applications, though, the data available tend to be small to medium size data sets. Further, these data sets typically are incomplete, contain entry errors, and unbalanced in terms of target classifications. For medical imaging data, beyond the basic complex nature of anatomical structures, there are challenges of noise from the collection process and the presence of imaging artifacts. The long-term objective of our research is to develop robust, accessible, rapid, transparent and accurate computer-aided medical diagnosis and grading tools to provide an automatic independent second opinion, to help avoid errors/oversights, to clinicians. The proposed research in our proposal aims to introduce or adapt new deep learning approaches to solve problems related to classification, segmentation, and interpretability for small- or medium-scale medical data. The classification problems considered are primarily related to the development of Computer Aided Diagnosis (CAD) systems, particularly for cytological images of fine needle biopsies for breast cancer, where we seek to improve accuracy and robustness to achieve clinically acceptable performance. We will continue our research on leveraging the progression of a disease over time to improve diagnosis. To create augmented data for deep learning training, we will study the creation of images and sequences of medical images by adapting our recent work on generative adversarial networks. For noisy medical data, we will seek to improve training in the presence of such data using local intrinsic dimensionality to locally evaluate the trained model. Our recent work on Continual Learning will be extended to improve the reliability of CAD systems to allow online learning of shifts in data distributions. Finally, we will our continue work on Meta-Learning by extending recent work on unsupervised Few-Shot Meta-Learning to radiomic applications. We will continue our work on improved nuclear segmentation algorithms for optical microscopic images. In addition to trying new deeper architectures, we will also explore the design of new loss functions for segmenting medical images, exploring the development of techniques beyond optimization approaches such as deep supervision, and the development of algorithmic learning rate schedulers. For deep learning models for malignancy diagnosis or grading, one major difficulty in these systems being approved for clinical use is the lack of interpretability of the decisions made by these models. To interpret the decisions made by deep learning models, we are studying correlations between handcrafted features and deep learning model features, the image retrieval strategies for finding similar images from past patients, and the generation of counterfactual visual explanations.